Intra-molecular coupling as a mechanism for a liquid-liquid phase transition

We study a model for water with a tunable intra-molecular interaction $J_\sigma$, using mean field theory and off-lattice Monte Carlo simulations. For all $J_\sigma\geq 0$, the model displays a temperature of maximum density.For a finite intra-molecular interaction $J_\sigma > 0$,our calculations support the presence of a liquid-liquid phase transition with a possible liquid-liquid critical point for water, likely pre-empted by inevitable freezing. For J=0 the liquid-liquid critical point disappears at T=0.Comment: 8 pages, 4 figure

Parametric POMDPs for planning in continuous state spaces

This thesis is concerned with planning and acting under uncertainty in partially-observable continuous domains. In particular, it focusses on the problem of mobile robot navigation given a known map. The dominant paradigm for robot localisation is to use Bayesian estimation to maintain a probability distribution over possible robot poses. In contrast, control algorithms often base their decisions on the assumption that a single state, such as the mode of this distribution, is correct. In scenarios involving significant uncertainty, this can lead to serious control errors. It is generally agreed that the reliability of navigation in uncertain environments would be greatly improved by the ability to consider the entire distribution when acting, rather than the single most likely state. The framework adopted in this thesis for modelling navigation problems mathematically is the Partially Observable Markov Decision Process (POMDP). An exact solution to a POMDP problem provides the optimal balance between reward-seeking behaviour and information-seeking behaviour, in the presence of sensor and actuation noise. Unfortunately, previous exact and approximate solution methods have had difficulty scaling to real applications. The contribution of this thesis is the formulation of an approach to planning in the space of continuous parameterised approximations to probability distributions. Theoretical and practical results are presented which show that, when compared with similar methods from the literature, this approach is capable of scaling to larger and more realistic problems. In order to apply the solution algorithm to real-world problems, a number of novel improvements are proposed. Specifically, Monte Carlo methods are employed to estimate distributions over future parameterised beliefs, improving planning accuracy without a loss of efficiency. Conditional independence assumptions are exploited to simplify the problem, reducing computational requirements. Scalability is further increased by focussing computation on likely beliefs, using metric indexing structures for efficient function approximation. Local online planning is incorporated to assist global offline planning, allowing the precision of the latter to be decreased without adversely affecting solution quality. Finally, the algorithm is implemented and demonstrated during real-time control of a mobile robot in a challenging navigation task. We argue that this task is substantially more challenging and realistic than previous problems to which POMDP solution methods have been applied. Results show that POMDP planning, which considers the evolution of the entire probability distribution over robot poses, produces significantly more robust behaviour when compared with a heuristic planner which considers only the most likely states and outcomes

Deficits, expectations and paradigms in British and American drug safety assessment: prising open the black box of regulatory science

This article examines the regulation of nonsteroidal anti-inflammatory drugs (NSAIDs), with particular focus on products approved for marketing in the United Kingdom, while denied marketing approval in the United States on safety grounds, and then subsequently withdrawn from the UK market on those grounds. Using international comparison of regulatory data never before accessed outside government and companies, together with interviews with relevant industry scientists and regulators, the article demonstrates the importance of regulatory expectations, deficits and paradigms. It is argued both that these sociological concepts can be enriched by their application to detailed comparative case study of regulatory science, and that they provide an important policy-relevant framework with which to understand discrepant drug regulatory processes in a sociohistorical context. It is found that regulatory expectations and paradigms may be regarded as mediating factors between political culture and structural interests, on the one hand, and the outcomes of regulatory science (including deficits), on the other